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Mount (Mt) Elgon forest in western Kenya is important for biodiversity, environmental protection and socio‐economic development. Characterizing forest conditions is essential for evaluation of sustainable management and conservation activities. This paper covers findings of a study which determined and analysed indicators useful in monitoring disturbance levels in the Mt Elgon Forest. A systematic survey was carried out and covered 305 plots of 0.02 ha and 250 smaller nested regeneration plots along 10 belt transects that were distributed in five blocks within the moist lower montane forest type. Collected and analysed data include types of disturbance, tree species composition, abundance and logged species. Correlation breakdown among disturbance types revealed that, paths were indicators of the number of tree harvesting sites (rs =1.00, P < 0.01) and of de‐vegetated areas through grass harvesting (rs = 0.90, P = 0.04). Solanum mauritianum Scop. was an indicator of old‐charcoal production sites. Logging targeted 13 tree species and harvested trees with diameter at breast height above 20 cm. The most exploited species were Olea capensis L. and Deinbolia kilimandscharica Taub. All exploited species had low regeneration but tree regeneration was not an effective indicator of logging.

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Forest measurements, especially in natural forests are cumbersome and complex. 100% enumeration is costly and inefficient. This study sought to find out reliable, efficient and cost-effective sampling schemes for use in tropical rain forest (TRF), moist montane forest (MMF) and dry woodland forest (DWF) in Kenya. Forty-eight sampling schemes (each combining sampling intensity (5, 10, 20, 30%), plot size (25, 50, 100, 400 m2) and sampling technique (simple random sampling, systematic sampling along North-South and along East-West orientations) were generated for testing estimates of forest attributes such as regeneration through simulation using R-software. Sampling error and effort were used to measure efficiency of each sampling scheme in relation to actual values. Though forest sites differed in biophysical characteristics, cost of sampling increased with decreasing plot size regardless of the forest type and attribute. Accuracy of inventory increased with decreasing plot size. Plot sizes that captured inherent variability were 5mx5m for regeneration and trees ha-1 across forest types but varied between forest types for basal area. Different sampling schemes were ranked for relative efficiency through simulation techniques, using regeneration as an example. In many instances systematic sampling-based sampling schemes were most effective. Sub-sampling in one-hectare forest unit gave reliable results in TRF (e.g. SSV-5mx5m-30%) and DWF (e.g. SSV-10mx10m-30%) but not in MMF (5mx5m-100%). One-hectare-complete-inventory method was found inevitable for regeneration assessment in montane forest.